Advertisement

Improved Bacterial Foraging Optimization Algorithm with Information Communication Mechanism for Nurse Scheduling

  • Ben NiuEmail author
  • Chao Wang
  • Jing Liu
  • Jianhou GanEmail author
  • Lingyun Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9226)

Abstract

As a NP-hard combinatorial problem, nurse scheduling problem (NSP) is a well-known personnel scheduling task whose goal is to create a nurse schedule under a series of hard and soft constraints in a practical world. In this paper, a variant of structure-redesigned-based bacterial foraging optimization (SRBFO) with a dynamic topology structure (SRBFO-DN) is employed for solving nurse scheduling problem (NSP). In SRBFO-DN, each bacterium achieves cooperation by information exchange mechanism switching the topology structure between star topology and ring topology. A special encoding operation of bacteria in SRBFO-DN is adopted to transform position vectors into feasible solutions, which can make SRBFO-DN successfully dealing with this typical difficult and discrete NSP. Experiment results obtained by SRBFO-DN compared with SRBFO and SPSO demonstrated that the efficiency of the proposed SRBFO-DN algorithm is better than other two algorithms for dealing with NSP.

Keywords

Bacterial foraging optimization Topology structure Nurse scheduling problem 

Notes

Acknowledgements

This work is partially supported by The National Natural Science Foundation of China (Grants nos. 71001072, 71271140, 71471158, 71461027, 61262071), the Natural Science Foundation of Guangdong Province (Grant nos. S2012010008668, 9451806001002294), Shenzhen Science and Technology Plan Project (Grant no. CXZZ20140418182638764).

References

  1. 1.
    Karp, R.M.: Reducibility Among Combinatorial Problems. Springer, US (1972)CrossRefGoogle Scholar
  2. 2.
    Al-Betar, M.A., Khader, A.T.: A harmony search algorithm for university course timetabling. Ann. Oper. Res. 194(1), 3–31 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Burke, E.K., Curtois, T., Qu, R.: A scatter search methodology for the nurse rostering problem. J. Oper. Res. Soc. 61(11), 1667–1679 (2010)CrossRefGoogle Scholar
  4. 4.
    Aickelin, U., Burke, E.K., Li, J.: An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering. J. Oper. Res. Soc. 58(12), 1574–1585 (2007)CrossRefGoogle Scholar
  5. 5.
    Kim, S.J., Ko, Y.W., Uhmn, S.: A strategy to improve performance of genetic algorithm for nurse scheduling problem. Int. J. Soft. Eng. Appl. 8(1), 53–62 (2014)Google Scholar
  6. 6.
    Rasip, N.M., Basari, A.S.H., Hussin, B.: A guided particle swarm optimization algorithm for nurse scheduling problem. Appl. Math. Sci. 8(113), 5625–5632 (2014)Google Scholar
  7. 7.
    Al-Betar, M.A., Khader, A.T., Nadi, F.: Selection mechanisms in memory consideration for examination timetabling with harmony search. In: 12th Annual Conference on Genetic and Evolutionary Computation, pp. 1203–1210. ACM Press, New York (2010)Google Scholar
  8. 8.
    Alatas, B.: Chaotic harmony search algorithms. Appl. Math. Comput. 216(9), 2687–2699 (2010)CrossRefGoogle Scholar
  9. 9.
    Niu, B., Liu, J., Bi, Y.: Improved bacterial foraging optimization algorithm with information communication mechanism. In: 2014 Tenth International Conference on Computational Intelligence and Security, pp. 47–51. IEEE Press, New York (2014)Google Scholar
  10. 10.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Burke, E.K., Causmaecker, P.D., Berghe, G.V.: The state of the art of nurse rostering. J. Sched. 7(6), 441–499 (2004)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Niu, B., Wang, H., Wang, J.W., Tan, L.J.: Multi-objective bacterial foraging optimization. Neurocomputing 116, 336–345 (2012)CrossRefGoogle Scholar
  13. 13.
    Niu, B., Wang, H., Chai, Y.J.: Bacterial colony optimization. Discrete Dyn. Nat. Soc. 2012, 28 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of ManagementShenzhen UniversityShenzhenChina
  2. 2.Department of Industrial and System EngineeringHong Kong Polytechnic UniversityHong KongChina
  3. 3.Key Laboratory of Educational Information for Nationalities, Ministry of EducationYunnan Normal UniversityKunmingChina

Personalised recommendations